Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations4482
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory569.3 B

Variable types

Text4
Numeric13
Categorical3

Alerts

52 Weeks High is highly overall correlated with 52 Weeks Low and 6 other fieldsHigh correlation
52 Weeks Low is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
Currency is highly overall correlated with EPS AnnualHigh correlation
EPS Annual is highly overall correlated with 52 Weeks High and 7 other fieldsHigh correlation
Market Cap (in M) is highly overall correlated with 52 Weeks High and 10 other fieldsHigh correlation
Performance (52 weeks) is highly overall correlated with Market Cap (in M) and 1 other fieldsHigh correlation
Price is highly overall correlated with 52 Weeks High and 7 other fieldsHigh correlation
Price 52 Weeks Ago is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
ROI Annual is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
Résultat net is highly overall correlated with 52 Weeks High and 6 other fieldsHigh correlation
Total assets is highly overall correlated with Market Cap (in M) and 2 other fieldsHigh correlation
Volume 1 month is highly overall correlated with Market Cap (in M) and 2 other fieldsHigh correlation
Volume 52 weeks is highly overall correlated with Market Cap (in M) and 2 other fieldsHigh correlation
Currency is highly imbalanced (92.2%)Imbalance
Price is highly skewed (γ1 = 26.65270708)Skewed
Market Cap (in M) is highly skewed (γ1 = 24.19047191)Skewed
Volume 52 weeks is highly skewed (γ1 = 42.91965738)Skewed
Volume 1 month is highly skewed (γ1 = 27.42692379)Skewed
52 Weeks High is highly skewed (γ1 = 33.84414493)Skewed
52 Weeks Low is highly skewed (γ1 = 22.951291)Skewed
Price 52 Weeks Ago is highly skewed (γ1 = 33.56960373)Skewed
EPS Annual is highly skewed (γ1 = 49.8061047)Skewed
ROI Annual is highly skewed (γ1 = -51.20066951)Skewed
Symbol has unique valuesUnique
Company Name has unique valuesUnique
Market Cap (in M) has unique valuesUnique
Beta has unique valuesUnique

Reproduction

Analysis started2024-08-13 14:35:33.190978
Analysis finished2024-08-13 14:36:26.536600
Duration53.35 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Symbol
Text

UNIQUE 

Distinct4482
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size265.5 KiB
2024-08-13T14:36:27.202188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.6289603
Min length1

Characters and Unicode

Total characters16265
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4482 ?
Unique (%)100.0%

Sample

1st rowTRNS
2nd rowACRV
3rd rowCOLM
4th rowMOVE
5th rowHCKT
ValueCountFrequency (%)
trns 1
 
< 0.1%
cndt 1
 
< 0.1%
hckt 1
 
< 0.1%
hcwb 1
 
< 0.1%
meip 1
 
< 0.1%
lfus 1
 
< 0.1%
govx 1
 
< 0.1%
itri 1
 
< 0.1%
ka 1
 
< 0.1%
colm 1
 
< 0.1%
Other values (4472) 4472
99.8%
2024-08-13T14:36:28.437114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 1166
 
7.2%
C 1150
 
7.1%
T 1097
 
6.7%
S 1080
 
6.6%
R 1072
 
6.6%
N 939
 
5.8%
L 836
 
5.1%
I 827
 
5.1%
M 743
 
4.6%
E 741
 
4.6%
Other values (16) 6614
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1166
 
7.2%
C 1150
 
7.1%
T 1097
 
6.7%
S 1080
 
6.6%
R 1072
 
6.6%
N 939
 
5.8%
L 836
 
5.1%
I 827
 
5.1%
M 743
 
4.6%
E 741
 
4.6%
Other values (16) 6614
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1166
 
7.2%
C 1150
 
7.1%
T 1097
 
6.7%
S 1080
 
6.6%
R 1072
 
6.6%
N 939
 
5.8%
L 836
 
5.1%
I 827
 
5.1%
M 743
 
4.6%
E 741
 
4.6%
Other values (16) 6614
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1166
 
7.2%
C 1150
 
7.1%
T 1097
 
6.7%
S 1080
 
6.6%
R 1072
 
6.6%
N 939
 
5.8%
L 836
 
5.1%
I 827
 
5.1%
M 743
 
4.6%
E 741
 
4.6%
Other values (16) 6614
40.7%

Company Name
Text

UNIQUE 

Distinct4482
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size335.6 KiB
2024-08-13T14:36:28.910884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length55
Median length39
Mean length19.654172
Min length2

Characters and Unicode

Total characters88090
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4482 ?
Unique (%)100.0%

Sample

1st rowTranscat Inc
2nd rowAcrivon Therapeutics Inc
3rd rowColumbia Sportswear Co
4th rowMovano Inc
5th rowHackett Group Inc
ValueCountFrequency (%)
inc 2843
 
21.0%
corp 824
 
6.1%
ltd 337
 
2.5%
holdings 333
 
2.5%
group 250
 
1.8%
co 185
 
1.4%
therapeutics 174
 
1.3%
financial 114
 
0.8%
technologies 110
 
0.8%
acquisition 96
 
0.7%
Other values (4772) 8296
61.2%
2024-08-13T14:36:29.677915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9080
 
10.3%
n 7520
 
8.5%
e 5933
 
6.7%
o 5593
 
6.3%
c 5340
 
6.1%
r 5177
 
5.9%
i 4994
 
5.7%
a 4909
 
5.6%
t 4046
 
4.6%
s 3548
 
4.0%
Other values (64) 31950
36.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 88090
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9080
 
10.3%
n 7520
 
8.5%
e 5933
 
6.7%
o 5593
 
6.3%
c 5340
 
6.1%
r 5177
 
5.9%
i 4994
 
5.7%
a 4909
 
5.6%
t 4046
 
4.6%
s 3548
 
4.0%
Other values (64) 31950
36.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 88090
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9080
 
10.3%
n 7520
 
8.5%
e 5933
 
6.7%
o 5593
 
6.3%
c 5340
 
6.1%
r 5177
 
5.9%
i 4994
 
5.7%
a 4909
 
5.6%
t 4046
 
4.6%
s 3548
 
4.0%
Other values (64) 31950
36.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 88090
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9080
 
10.3%
n 7520
 
8.5%
e 5933
 
6.7%
o 5593
 
6.3%
c 5340
 
6.1%
r 5177
 
5.9%
i 4994
 
5.7%
a 4909
 
5.6%
t 4046
 
4.6%
s 3548
 
4.0%
Other values (64) 31950
36.3%

Price
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3343
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.388719
Minimum0.0503
Maximum8506.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.1 KiB
2024-08-13T14:36:30.006890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0503
5-th percentile0.63
Q13.4
median13.19
Q344.92
95-th percentile200.9025
Maximum8506.24
Range8506.1897
Interquartile range (IQR)41.52

Descriptive statistics

Standard deviation181.96383
Coefficient of variation (CV)3.5409294
Kurtosis1096.5023
Mean51.388719
Median Absolute Deviation (MAD)11.804
Skewness26.652707
Sum230324.24
Variance33110.834
MonotonicityNot monotonic
2024-08-13T14:36:30.275970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.01 8
 
0.2%
1.06 8
 
0.2%
1.47 7
 
0.2%
1.6 7
 
0.2%
11.45 7
 
0.2%
1.65 7
 
0.2%
1.02 7
 
0.2%
1.4 7
 
0.2%
1.04 6
 
0.1%
2.4 6
 
0.1%
Other values (3333) 4412
98.4%
ValueCountFrequency (%)
0.0503 1
< 0.1%
0.075 1
< 0.1%
0.076 1
< 0.1%
0.0766 1
< 0.1%
0.08 1
< 0.1%
0.0823 1
< 0.1%
0.092 1
< 0.1%
0.0933 1
< 0.1%
0.0945 1
< 0.1%
0.0978 1
< 0.1%
ValueCountFrequency (%)
8506.24 1
< 0.1%
3443.05 1
< 0.1%
3120.25 1
< 0.1%
1974.15 1
< 0.1%
1883.62 1
< 0.1%
1752.25 1
< 0.1%
1701.48 1
< 0.1%
1521.92 1
< 0.1%
1397.26 1
< 0.1%
1259.41 1
< 0.1%

Market Cap (in M)
Real number (ℝ)

HIGH CORRELATION  SKEWED  UNIQUE 

Distinct4482
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12562.562
Minimum0.20246208
Maximum3287742.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.1 KiB
2024-08-13T14:36:30.614221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.20246208
5-th percentile6.5080889
Q195.97891
median706.08459
Q34138.6571
95-th percentile42628.523
Maximum3287742.5
Range3287742.3
Interquartile range (IQR)4042.6782

Descriptive statistics

Standard deviation96509.919
Coefficient of variation (CV)7.6823436
Kurtosis692.56512
Mean12562.562
Median Absolute Deviation (MAD)690.21847
Skewness24.190472
Sum56305404
Variance9.3141646 × 109
MonotonicityNot monotonic
2024-08-13T14:36:30.910161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1051.552692 1
 
< 0.1%
3556.372052 1
 
< 0.1%
131.910937 1
 
< 0.1%
11157.58663 1
 
< 0.1%
38901.79531 1
 
< 0.1%
4240.310905 1
 
< 0.1%
25967.83614 1
 
< 0.1%
4306.491217 1
 
< 0.1%
11553.67616 1
 
< 0.1%
4822.75805 1
 
< 0.1%
Other values (4472) 4472
99.8%
ValueCountFrequency (%)
0.202462083 1
< 0.1%
0.290874472 1
< 0.1%
0.3662907246 1
< 0.1%
0.5117001326 1
< 0.1%
0.6849636886 1
< 0.1%
0.6859719465 1
< 0.1%
0.727851 1
< 0.1%
0.7453370722 1
< 0.1%
0.7492588667 1
< 0.1%
0.7598715686 1
< 0.1%
ValueCountFrequency (%)
3287742.486 1
< 0.1%
3017962.34 1
< 0.1%
2581647.096 1
< 0.1%
2024988.839 1
< 0.1%
1752130.051 1
< 0.1%
1309863.215 1
< 0.1%
847457.4806 1
< 0.1%
690133.0242 1
< 0.1%
638928.0644 1
< 0.1%
585534.9078 1
< 0.1%

Beta
Real number (ℝ)

UNIQUE 

Distinct4482
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.068385
Minimum-6.9775743
Maximum19.570795
Zeros0
Zeros (%)0.0%
Negative423
Negative (%)9.4%
Memory size35.1 KiB
2024-08-13T14:36:31.203238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-6.9775743
5-th percentile-0.22453388
Q10.44216632
median0.937994
Q31.5539674
95-th percentile2.8683151
Maximum19.570795
Range26.548369
Interquartile range (IQR)1.111801

Descriptive statistics

Standard deviation1.0771481
Coefficient of variation (CV)1.0082022
Kurtosis25.588223
Mean1.068385
Median Absolute Deviation (MAD)0.54634578
Skewness1.9493479
Sum4788.5016
Variance1.1602481
MonotonicityNot monotonic
2024-08-13T14:36:31.492952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9319894 1
 
< 0.1%
2.585754 1
 
< 0.1%
-0.0058279326 1
 
< 0.1%
0.7019731 1
 
< 0.1%
0.46431598 1
 
< 0.1%
2.0908768 1
 
< 0.1%
1.1370689 1
 
< 0.1%
1.1250257 1
 
< 0.1%
1.0901773 1
 
< 0.1%
1.2514409 1
 
< 0.1%
Other values (4472) 4472
99.8%
ValueCountFrequency (%)
-6.9775743 1
< 0.1%
-4.9759016 1
< 0.1%
-4.3898983 1
< 0.1%
-3.8894632 1
< 0.1%
-3.6600218 1
< 0.1%
-3.375102 1
< 0.1%
-3.0445251 1
< 0.1%
-3.043933 1
< 0.1%
-2.8673244 1
< 0.1%
-2.8665426 1
< 0.1%
ValueCountFrequency (%)
19.570795 1
< 0.1%
11.313087 1
< 0.1%
9.652831 1
< 0.1%
8.4484005 1
< 0.1%
8.446643 1
< 0.1%
7.075054 1
< 0.1%
7.0186133 1
< 0.1%
6.7305875 1
< 0.1%
6.5846024 1
< 0.1%
5.979147 1
< 0.1%

Volume 52 weeks
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4480
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1554596.6
Minimum849.20635
Maximum4.6049593 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.1 KiB
2024-08-13T14:36:31.799216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum849.20635
5-th percentile12732.472
Q1105516.02
median420115.48
Q31223126.6
95-th percentile5540663.2
Maximum4.6049593 × 108
Range4.6049508 × 108
Interquartile range (IQR)1117610.6

Descriptive statistics

Standard deviation8041604.2
Coefficient of variation (CV)5.1727914
Kurtosis2379.2571
Mean1554596.6
Median Absolute Deviation (MAD)371902.18
Skewness42.919657
Sum6.9677021 × 109
Variance6.4667397 × 1013
MonotonicityNot monotonic
2024-08-13T14:36:32.101719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225453.9683 2
 
< 0.1%
6648.015873 2
 
< 0.1%
92715.47619 1
 
< 0.1%
3511790.476 1
 
< 0.1%
1866615.476 1
 
< 0.1%
487720.6349 1
 
< 0.1%
74468.25397 1
 
< 0.1%
192685.3175 1
 
< 0.1%
15696701.59 1
 
< 0.1%
1345657.143 1
 
< 0.1%
Other values (4470) 4470
99.7%
ValueCountFrequency (%)
849.2063492 1
< 0.1%
935.059761 1
< 0.1%
1153.174603 1
< 0.1%
1188.095238 1
< 0.1%
1755.952381 1
< 0.1%
1777.777778 1
< 0.1%
1797.619048 1
< 0.1%
2017.063492 1
< 0.1%
2108.333333 1
< 0.1%
2126.587302 1
< 0.1%
ValueCountFrequency (%)
460495926.5 1
< 0.1%
107475531.3 1
< 0.1%
60843084.92 1
< 0.1%
60546926.98 1
< 0.1%
56703451.59 1
< 0.1%
53449109.52 1
< 0.1%
53207215.08 1
< 0.1%
52205071.83 1
< 0.1%
49901964.06 1
< 0.1%
46498527.38 1
< 0.1%

Volume 1 month
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4425
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1731342.8
Minimum65.217391
Maximum3.4773365 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.1 KiB
2024-08-13T14:36:32.383956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum65.217391
5-th percentile9114.5652
Q192601.087
median428802.17
Q31321422.8
95-th percentile6702045.9
Maximum3.4773365 × 108
Range3.4773358 × 108
Interquartile range (IQR)1228821.7

Descriptive statistics

Standard deviation7286573.1
Coefficient of variation (CV)4.2086253
Kurtosis1169.7453
Mean1731342.8
Median Absolute Deviation (MAD)394206.52
Skewness27.426924
Sum7.7598784 × 109
Variance5.3094148 × 1013
MonotonicityNot monotonic
2024-08-13T14:36:32.689054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52026.08696 3
 
0.1%
81686.95652 3
 
0.1%
2434.782609 2
 
< 0.1%
24913.04348 2
 
< 0.1%
29304.34783 2
 
< 0.1%
839.1304348 2
 
< 0.1%
83213.04348 2
 
< 0.1%
55626.08696 2
 
< 0.1%
54586.95652 2
 
< 0.1%
1556.521739 2
 
< 0.1%
Other values (4415) 4460
99.5%
ValueCountFrequency (%)
65.2173913 1
< 0.1%
73.91304348 1
< 0.1%
139.1304348 1
< 0.1%
181.8181818 1
< 0.1%
182.6086957 1
< 0.1%
213.0434783 1
< 0.1%
217.3913043 2
< 0.1%
362.3043478 1
< 0.1%
468.1818182 1
< 0.1%
500 1
< 0.1%
ValueCountFrequency (%)
347733646.8 1
< 0.1%
110366273.9 1
< 0.1%
108802565.2 1
< 0.1%
81539721.74 1
< 0.1%
78406543.48 1
< 0.1%
63759239.13 1
< 0.1%
60902782.61 1
< 0.1%
59622121.74 1
< 0.1%
58253669.57 1
< 0.1%
53438513.04 1
< 0.1%

52 Weeks High
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3519
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.452395
Minimum0.87
Maximum14400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.1 KiB
2024-08-13T14:36:32.997893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.87
5-th percentile2.36
Q18.7
median21.812
Q361.1025
95-th percentile247.595
Maximum14400
Range14399.13
Interquartile range (IQR)52.4025

Descriptive statistics

Standard deviation291.34357
Coefficient of variation (CV)4.2561487
Kurtosis1491.9859
Mean68.452395
Median Absolute Deviation (MAD)16.9695
Skewness33.844145
Sum306803.63
Variance84881.075
MonotonicityNot monotonic
2024-08-13T14:36:33.274787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.05 8
 
0.2%
13 8
 
0.2%
2.1 7
 
0.2%
14 7
 
0.2%
3.5 6
 
0.1%
3.6 6
 
0.1%
12.5 5
 
0.1%
7.75 5
 
0.1%
2.55 5
 
0.1%
4.55 5
 
0.1%
Other values (3509) 4420
98.6%
ValueCountFrequency (%)
0.87 1
< 0.1%
0.8999 2
< 0.1%
0.9039 1
< 0.1%
0.909 1
< 0.1%
0.93 1
< 0.1%
0.94 1
< 0.1%
0.98 2
< 0.1%
0.99 1
< 0.1%
1.01 1
< 0.1%
1.02 1
< 0.1%
ValueCountFrequency (%)
14400 1
< 0.1%
8700 1
< 0.1%
4144.32 1
< 0.1%
3242.54 1
< 0.1%
2173.01 1
< 0.1%
1905.09 1
< 0.1%
1899.21 1
< 0.1%
1759.76 1
< 0.1%
1670.24 1
< 0.1%
1535.86 1
< 0.1%

52 Weeks Low
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3292
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.580585
Minimum0.0004
Maximum5210.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.1 KiB
2024-08-13T14:36:33.557652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0004
5-th percentile0.445805
Q12.2
median10.3
Q333.04875
95-th percentile147.61
Maximum5210.49
Range5210.4896
Interquartile range (IQR)30.84875

Descriptive statistics

Standard deviation121.82165
Coefficient of variation (CV)3.3302271
Kurtosis822.38084
Mean36.580585
Median Absolute Deviation (MAD)9.19995
Skewness22.951291
Sum163954.18
Variance14840.515
MonotonicityNot monotonic
2024-08-13T14:36:33.868382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.75 11
 
0.2%
0.65 9
 
0.2%
1.21 9
 
0.2%
1.04 9
 
0.2%
1 9
 
0.2%
0.7 9
 
0.2%
1.37 8
 
0.2%
1.55 8
 
0.2%
1.25 8
 
0.2%
1.03 8
 
0.2%
Other values (3282) 4394
98.0%
ValueCountFrequency (%)
0.0004 1
< 0.1%
0.038 1
< 0.1%
0.048 1
< 0.1%
0.056 1
< 0.1%
0.064 1
< 0.1%
0.07 1
< 0.1%
0.0712 1
< 0.1%
0.0734 1
< 0.1%
0.0771 1
< 0.1%
0.0811 1
< 0.1%
ValueCountFrequency (%)
5210.49 1
< 0.1%
2735.3 1
< 0.1%
2379.02 1
< 0.1%
1401.0101 1
< 0.1%
1295.65 1
< 0.1%
1274.91 1
< 0.1%
1141.04 1
< 0.1%
930.72 1
< 0.1%
860.1 1
< 0.1%
811.99 1
< 0.1%

Exchange
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size272.7 KiB
NASDAQ
2847 
NYSE
1635 

Length

Max length6
Median length6
Mean length5.270415
Min length4

Characters and Unicode

Total characters23622
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNASDAQ
2nd rowNASDAQ
3rd rowNASDAQ
4th rowNASDAQ
5th rowNASDAQ

Common Values

ValueCountFrequency (%)
NASDAQ 2847
63.5%
NYSE 1635
36.5%

Length

2024-08-13T14:36:34.146486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-13T14:36:34.411785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nasdaq 2847
63.5%
nyse 1635
36.5%

Most occurring characters

ValueCountFrequency (%)
A 5694
24.1%
N 4482
19.0%
S 4482
19.0%
D 2847
12.1%
Q 2847
12.1%
Y 1635
 
6.9%
E 1635
 
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23622
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 5694
24.1%
N 4482
19.0%
S 4482
19.0%
D 2847
12.1%
Q 2847
12.1%
Y 1635
 
6.9%
E 1635
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23622
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 5694
24.1%
N 4482
19.0%
S 4482
19.0%
D 2847
12.1%
Q 2847
12.1%
Y 1635
 
6.9%
E 1635
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23622
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 5694
24.1%
N 4482
19.0%
S 4482
19.0%
D 2847
12.1%
Q 2847
12.1%
Y 1635
 
6.9%
E 1635
 
6.9%

Performance (52 weeks)
Real number (ℝ)

HIGH CORRELATION 

Distinct4475
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0094737247
Minimum-0.99985424
Maximum33.280986
Zeros0
Zeros (%)0.0%
Negative2322
Negative (%)51.8%
Memory size35.1 KiB
2024-08-13T14:36:34.665154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.99985424
5-th percentile-0.85648093
Q1-0.36312963
median-0.018677708
Q30.22016871
95-th percentile0.79503351
Maximum33.280986
Range34.28084
Interquartile range (IQR)0.58329835

Descriptive statistics

Standard deviation0.79713482
Coefficient of variation (CV)-84.141649
Kurtosis690.2903
Mean-0.0094737247
Median Absolute Deviation (MAD)0.28149938
Skewness17.973417
Sum-42.461234
Variance0.63542392
MonotonicityNot monotonic
2024-08-13T14:36:35.440071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1670839982 2
 
< 0.1%
-0.47592853 2
 
< 0.1%
-0.8042474026 2
 
< 0.1%
0.387608379 2
 
< 0.1%
-0.06056350255 2
 
< 0.1%
0.07029265643 2
 
< 0.1%
-0.5009512193 2
 
< 0.1%
0.2544230824 1
 
< 0.1%
0.2815539887 1
 
< 0.1%
-0.4017006624 1
 
< 0.1%
Other values (4465) 4465
99.6%
ValueCountFrequency (%)
-0.9998542374 1
< 0.1%
-0.9994216567 1
< 0.1%
-0.9992965079 1
< 0.1%
-0.9982727312 1
< 0.1%
-0.9979352051 1
< 0.1%
-0.9973795645 1
< 0.1%
-0.9973444564 1
< 0.1%
-0.9965653988 1
< 0.1%
-0.9964575223 1
< 0.1%
-0.9963780463 1
< 0.1%
ValueCountFrequency (%)
33.28098556 1
< 0.1%
8.725687571 1
< 0.1%
7.564530876 1
< 0.1%
7.492914449 1
< 0.1%
6.921500924 1
< 0.1%
5.897939474 1
< 0.1%
5.736607268 1
< 0.1%
5.64065769 1
< 0.1%
5.450054284 1
< 0.1%
5.299123385 1
< 0.1%
Distinct51
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size258.4 KiB
2024-08-13T14:36:35.740341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters8964
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)0.3%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 3818
85.2%
cn 169
 
3.8%
il 78
 
1.7%
gb 56
 
1.2%
ca 53
 
1.2%
sg 34
 
0.8%
hk 32
 
0.7%
ie 29
 
0.6%
bm 29
 
0.6%
ky 18
 
0.4%
Other values (41) 166
 
3.7%
2024-08-13T14:36:36.274092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 3860
43.1%
U 3841
42.8%
C 250
 
2.8%
N 183
 
2.0%
I 116
 
1.3%
G 108
 
1.2%
L 101
 
1.1%
B 97
 
1.1%
A 67
 
0.7%
K 57
 
0.6%
Other values (15) 284
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8964
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 3860
43.1%
U 3841
42.8%
C 250
 
2.8%
N 183
 
2.0%
I 116
 
1.3%
G 108
 
1.2%
L 101
 
1.1%
B 97
 
1.1%
A 67
 
0.7%
K 57
 
0.6%
Other values (15) 284
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8964
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 3860
43.1%
U 3841
42.8%
C 250
 
2.8%
N 183
 
2.0%
I 116
 
1.3%
G 108
 
1.2%
L 101
 
1.1%
B 97
 
1.1%
A 67
 
0.7%
K 57
 
0.6%
Other values (15) 284
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8964
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 3860
43.1%
U 3841
42.8%
C 250
 
2.8%
N 183
 
2.0%
I 116
 
1.3%
G 108
 
1.2%
L 101
 
1.1%
B 97
 
1.1%
A 67
 
0.7%
K 57
 
0.6%
Other values (15) 284
 
3.2%

Résultat net
Real number (ℝ)

HIGH CORRELATION 

Distinct4447
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7300024 × 108
Minimum-2.1601255 × 1010
Maximum1.2241905 × 1011
Zeros0
Zeros (%)0.0%
Negative2105
Negative (%)47.0%
Memory size35.1 KiB
2024-08-13T14:36:36.583233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2.1601255 × 1010
5-th percentile-2.8771373 × 108
Q1-36882000
median2761843
Q31.4685775 × 108
95-th percentile1.9954143 × 109
Maximum1.2241905 × 1011
Range1.4402031 × 1011
Interquartile range (IQR)1.8373975 × 108

Descriptive statistics

Standard deviation4.0519237 × 109
Coefficient of variation (CV)8.5664305
Kurtosis450.53581
Mean4.7300024 × 108
Median Absolute Deviation (MAD)68221500
Skewness19.262915
Sum2.1199871 × 1012
Variance1.6418085 × 1019
MonotonicityNot monotonic
2024-08-13T14:36:36.886956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
714000000 3
 
0.1%
1052000000 3
 
0.1%
290000000 2
 
< 0.1%
2560000000 2
 
< 0.1%
81000000 2
 
< 0.1%
-7000000 2
 
< 0.1%
4487000064 2
 
< 0.1%
-35390000 2
 
< 0.1%
-40105000 2
 
< 0.1%
-9636000 2
 
< 0.1%
Other values (4437) 4460
99.5%
ValueCountFrequency (%)
-2.160125542 × 10101
< 0.1%
-1.176899994 × 10101
< 0.1%
-9406706688 1
< 0.1%
-6808999936 1
< 0.1%
-6541000192 1
< 0.1%
-5866999808 1
< 0.1%
-5810999808 1
< 0.1%
-5791000064 1
< 0.1%
-5783000064 1
< 0.1%
-4943179776 1
< 0.1%
ValueCountFrequency (%)
1.224190525 × 10111
< 0.1%
1.019560018 × 10111
< 0.1%
8.813599949 × 10101
< 0.1%
8.765699686 × 10101
< 0.1%
7.992333926 × 10101
< 0.1%
7.974100173 × 10101
< 0.1%
5.221699994 × 10101
< 0.1%
5.143400038 × 10101
< 0.1%
4.441899827 × 10101
< 0.1%
4.259799859 × 10101
< 0.1%

Sector
Categorical

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size307.2 KiB
Healthcare
992 
Financial Services
740 
Technology
672 
Industrials
536 
Consumer Cyclical
494 
Other values (6)
1048 

Length

Max length22
Median length18
Mean length13.146809
Min length6

Characters and Unicode

Total characters58924
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndustrials
2nd rowHealthcare
3rd rowConsumer Cyclical
4th rowHealthcare
5th rowTechnology

Common Values

ValueCountFrequency (%)
Healthcare 992
22.1%
Financial Services 740
16.5%
Technology 672
15.0%
Industrials 536
12.0%
Consumer Cyclical 494
11.0%
Real Estate 227
 
5.1%
Consumer Defensive 206
 
4.6%
Communication Services 194
 
4.3%
Energy 178
 
4.0%
Basic Materials 157
 
3.5%

Length

2024-08-13T14:36:37.197279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
healthcare 992
15.3%
services 934
14.4%
financial 740
11.4%
consumer 700
10.8%
technology 672
10.3%
industrials 536
8.2%
cyclical 494
7.6%
real 227
 
3.5%
estate 227
 
3.5%
defensive 206
 
3.2%
Other values (5) 772
11.9%

Most occurring characters

ValueCountFrequency (%)
e 6717
11.4%
a 5613
 
9.5%
c 4677
 
7.9%
i 4610
 
7.8%
l 4398
 
7.5%
n 4160
 
7.1%
s 3539
 
6.0%
r 3497
 
5.9%
t 2505
 
4.3%
o 2432
 
4.1%
Other values (21) 16776
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58924
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6717
11.4%
a 5613
 
9.5%
c 4677
 
7.9%
i 4610
 
7.8%
l 4398
 
7.5%
n 4160
 
7.1%
s 3539
 
6.0%
r 3497
 
5.9%
t 2505
 
4.3%
o 2432
 
4.1%
Other values (21) 16776
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58924
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6717
11.4%
a 5613
 
9.5%
c 4677
 
7.9%
i 4610
 
7.8%
l 4398
 
7.5%
n 4160
 
7.1%
s 3539
 
6.0%
r 3497
 
5.9%
t 2505
 
4.3%
o 2432
 
4.1%
Other values (21) 16776
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58924
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6717
11.4%
a 5613
 
9.5%
c 4677
 
7.9%
i 4610
 
7.8%
l 4398
 
7.5%
n 4160
 
7.1%
s 3539
 
6.0%
r 3497
 
5.9%
t 2505
 
4.3%
o 2432
 
4.1%
Other values (21) 16776
28.5%
Distinct144
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size335.1 KiB
2024-08-13T14:36:37.573324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length40
Median length32
Mean length19.539491
Min length4

Characters and Unicode

Total characters87576
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowIndustrial Distribution
2nd rowBiotechnology
3rd rowApparel Manufacturing
4th rowMedical Devices
5th rowInformation Technology Services
ValueCountFrequency (%)
2205
 
18.9%
biotechnology 561
 
4.8%
services 428
 
3.7%
software 342
 
2.9%
banks 293
 
2.5%
specialty 290
 
2.5%
regional 288
 
2.5%
medical 232
 
2.0%
application 204
 
1.7%
equipment 194
 
1.7%
Other values (189) 6629
56.8%
2024-08-13T14:36:38.447392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8291
 
9.5%
7184
 
8.2%
i 6618
 
7.6%
t 5882
 
6.7%
a 5738
 
6.6%
n 5668
 
6.5%
o 5133
 
5.9%
r 4468
 
5.1%
s 4329
 
4.9%
c 4122
 
4.7%
Other values (38) 30143
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87576
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8291
 
9.5%
7184
 
8.2%
i 6618
 
7.6%
t 5882
 
6.7%
a 5738
 
6.6%
n 5668
 
6.5%
o 5133
 
5.9%
r 4468
 
5.1%
s 4329
 
4.9%
c 4122
 
4.7%
Other values (38) 30143
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87576
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8291
 
9.5%
7184
 
8.2%
i 6618
 
7.6%
t 5882
 
6.7%
a 5738
 
6.6%
n 5668
 
6.5%
o 5133
 
5.9%
r 4468
 
5.1%
s 4329
 
4.9%
c 4122
 
4.7%
Other values (38) 30143
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87576
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8291
 
9.5%
7184
 
8.2%
i 6618
 
7.6%
t 5882
 
6.7%
a 5738
 
6.6%
n 5668
 
6.5%
o 5133
 
5.9%
r 4468
 
5.1%
s 4329
 
4.9%
c 4122
 
4.7%
Other values (38) 30143
34.4%

Price 52 Weeks Ago
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3593
Distinct (%)80.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.968433
Minimum0.30000001
Maximum11250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.1 KiB
2024-08-13T14:36:38.946578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.30000001
5-th percentile1.46
Q16.0100002
median15.371808
Q344.969559
95-th percentile182.88526
Maximum11250
Range11249.7
Interquartile range (IQR)38.959558

Descriptive statistics

Standard deviation226.24692
Coefficient of variation (CV)4.4389617
Kurtosis1481.9702
Mean50.968433
Median Absolute Deviation (MAD)12.391808
Skewness33.569604
Sum228440.52
Variance51187.671
MonotonicityNot monotonic
2024-08-13T14:36:39.425827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.69999981 8
 
0.2%
1.220000029 8
 
0.2%
2.049999952 8
 
0.2%
3.019999981 7
 
0.2%
1.679999948 7
 
0.2%
4.199999809 7
 
0.2%
12 7
 
0.2%
3.779999971 6
 
0.1%
2.559999943 6
 
0.1%
7.400000095 6
 
0.1%
Other values (3583) 4412
98.4%
ValueCountFrequency (%)
0.3000000119 1
< 0.1%
0.400000006 1
< 0.1%
0.4300000072 1
< 0.1%
0.4379999936 1
< 0.1%
0.4799999893 1
< 0.1%
0.5009999871 1
< 0.1%
0.5099999905 1
< 0.1%
0.5170000196 1
< 0.1%
0.5210062861 1
< 0.1%
0.5320000052 1
< 0.1%
ValueCountFrequency (%)
11250 1
< 0.1%
6156.72998 1
< 0.1%
3456 1
< 0.1%
3190.70166 1
< 0.1%
2483.830078 1
< 0.1%
1566.987183 1
< 0.1%
1506.199951 1
< 0.1%
1464.240601 1
< 0.1%
1330 1
< 0.1%
1239.800049 1
< 0.1%

Currency
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size262.7 KiB
USD
4307 
CNY
 
92
EUR
 
27
CAD
 
11
BRL
 
10
Other values (15)
 
35

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13446
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowUSD
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 4307
96.1%
CNY 92
 
2.1%
EUR 27
 
0.6%
CAD 11
 
0.2%
BRL 10
 
0.2%
GBP 7
 
0.2%
CHF 4
 
0.1%
JPY 4
 
0.1%
AUD 3
 
0.1%
INR 3
 
0.1%
Other values (10) 14
 
0.3%

Length

2024-08-13T14:36:39.919703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usd 4307
96.1%
cny 92
 
2.1%
eur 27
 
0.6%
cad 11
 
0.2%
brl 10
 
0.2%
gbp 7
 
0.2%
chf 4
 
0.1%
jpy 4
 
0.1%
aud 3
 
0.1%
inr 3
 
0.1%
Other values (10) 14
 
0.3%

Most occurring characters

ValueCountFrequency (%)
U 4337
32.3%
D 4326
32.2%
S 4310
32.1%
C 107
 
0.8%
Y 99
 
0.7%
N 98
 
0.7%
R 45
 
0.3%
E 29
 
0.2%
B 17
 
0.1%
A 15
 
0.1%
Other values (13) 63
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13446
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 4337
32.3%
D 4326
32.2%
S 4310
32.1%
C 107
 
0.8%
Y 99
 
0.7%
N 98
 
0.7%
R 45
 
0.3%
E 29
 
0.2%
B 17
 
0.1%
A 15
 
0.1%
Other values (13) 63
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13446
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 4337
32.3%
D 4326
32.2%
S 4310
32.1%
C 107
 
0.8%
Y 99
 
0.7%
N 98
 
0.7%
R 45
 
0.3%
E 29
 
0.2%
B 17
 
0.1%
A 15
 
0.1%
Other values (13) 63
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13446
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 4337
32.3%
D 4326
32.2%
S 4310
32.1%
C 107
 
0.8%
Y 99
 
0.7%
N 98
 
0.7%
R 45
 
0.3%
E 29
 
0.2%
B 17
 
0.1%
A 15
 
0.1%
Other values (13) 63
 
0.5%

Total assets
Real number (ℝ)

HIGH CORRELATION 

Distinct4473
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6808261 × 108
Minimum1024
Maximum1.52041 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size35.1 KiB
2024-08-13T14:36:40.221674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1024
5-th percentile3520714
Q118063300
median50903000
Q31.3179875 × 108
95-th percentile6.0919552 × 108
Maximum1.52041 × 1010
Range1.5204099 × 1010
Interquartile range (IQR)1.1373545 × 108

Descriptive statistics

Standard deviation5.3737641 × 108
Coefficient of variation (CV)3.197097
Kurtosis225.82078
Mean1.6808261 × 108
Median Absolute Deviation (MAD)40610800
Skewness12.246744
Sum7.5334627 × 1011
Variance2.8877341 × 1017
MonotonicityNot monotonic
2024-08-13T14:36:40.718524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132670000 2
 
< 0.1%
69067000 2
 
< 0.1%
100625000 2
 
< 0.1%
144976992 2
 
< 0.1%
46608800 2
 
< 0.1%
116454000 2
 
< 0.1%
28483600 2
 
< 0.1%
37457200 2
 
< 0.1%
14000000 2
 
< 0.1%
112977000 1
 
< 0.1%
Other values (4463) 4463
99.6%
ValueCountFrequency (%)
1024 1
< 0.1%
12443 1
< 0.1%
113809 1
< 0.1%
164495 1
< 0.1%
228025 1
< 0.1%
333008 1
< 0.1%
360600 1
< 0.1%
376141 1
< 0.1%
393449 1
< 0.1%
396368 1
< 0.1%
ValueCountFrequency (%)
1.52041001 × 10101
< 0.1%
1.049559962 × 10101
< 0.1%
8043539968 1
< 0.1%
7759580160 1
< 0.1%
7433039872 1
< 0.1%
7170240000 1
< 0.1%
6478000000 1
< 0.1%
6365200000 1
< 0.1%
5858999808 1
< 0.1%
5666699776 1
< 0.1%

EPS Annual
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4402
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49664431
Minimum-2997.8
Maximum11880.286
Zeros0
Zeros (%)0.0%
Negative2059
Negative (%)45.9%
Memory size35.1 KiB
2024-08-13T14:36:41.233432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2997.8
5-th percentile-9.708875
Q1-1.127375
median0.1537
Q32.287075
95-th percentile10.02196
Maximum11880.286
Range14878.086
Interquartile range (IQR)3.41445

Descriptive statistics

Standard deviation194.47488
Coefficient of variation (CV)391.57779
Kurtosis3131.0725
Mean0.49664431
Median Absolute Deviation (MAD)1.64145
Skewness49.806105
Sum2225.9598
Variance37820.48
MonotonicityNot monotonic
2024-08-13T14:36:41.685322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6003 3
 
0.1%
-0.0212 3
 
0.1%
-0.0216 2
 
< 0.1%
2.339 2
 
< 0.1%
0.1243 2
 
< 0.1%
0.895 2
 
< 0.1%
-0.3885 2
 
< 0.1%
-0.6398 2
 
< 0.1%
-0.214 2
 
< 0.1%
0.1052 2
 
< 0.1%
Other values (4392) 4460
99.5%
ValueCountFrequency (%)
-2997.8 1
< 0.1%
-2181 1
< 0.1%
-1586.1632 1
< 0.1%
-1564.215 1
< 0.1%
-1018.2512 1
< 0.1%
-1008.1846 1
< 0.1%
-471.8591 1
< 0.1%
-434.6842 1
< 0.1%
-355.2372 1
< 0.1%
-309.1471 1
< 0.1%
ValueCountFrequency (%)
11880.2857 1
< 0.1%
2241.184 1
< 0.1%
788.6044 1
< 0.1%
463.3511 1
< 0.1%
201.4798 1
< 0.1%
149.2047 1
< 0.1%
133.0526 1
< 0.1%
117.4103 1
< 0.1%
92.8812 1
< 0.1%
77.818 1
< 0.1%

ROI Annual
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3324
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-69.659607
Minimum-108880
Maximum12633.08
Zeros0
Zeros (%)0.0%
Negative2038
Negative (%)45.5%
Memory size35.1 KiB
2024-08-13T14:36:42.013360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-108880
5-th percentile-157.534
Q1-26.3575
median1.41
Q38.24
95-th percentile24.4115
Maximum12633.08
Range121513.08
Interquartile range (IQR)34.5975

Descriptive statistics

Standard deviation1821.3121
Coefficient of variation (CV)-26.145886
Kurtosis2921.4261
Mean-69.659607
Median Absolute Deviation (MAD)10.54
Skewness-51.20067
Sum-312214.36
Variance3317177.8
MonotonicityNot monotonic
2024-08-13T14:36:42.293549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.15 7
 
0.2%
4.19 7
 
0.2%
4.53 7
 
0.2%
3.2 6
 
0.1%
9.32 6
 
0.1%
1.17 6
 
0.1%
1.93 5
 
0.1%
3.7 5
 
0.1%
9.05 5
 
0.1%
4.02 5
 
0.1%
Other values (3314) 4423
98.7%
ValueCountFrequency (%)
-108880 1
< 0.1%
-41952.85 1
< 0.1%
-29104.88 1
< 0.1%
-9780 1
< 0.1%
-5196.477 1
< 0.1%
-5033.59 1
< 0.1%
-3715.82 1
< 0.1%
-3645.8 1
< 0.1%
-3304.87 1
< 0.1%
-3184.29 1
< 0.1%
ValueCountFrequency (%)
12633.08 1
< 0.1%
2662.67 1
< 0.1%
1875.53 1
< 0.1%
1670 1
< 0.1%
1054.47 1
< 0.1%
737.69 1
< 0.1%
643.41 1
< 0.1%
445.65 1
< 0.1%
432.25 1
< 0.1%
307.73 1
< 0.1%

Interactions

2024-08-13T14:36:21.194298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:34.622787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:37.912100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:41.331075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:46.871292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:50.198991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:53.674301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:58.126795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:01.966460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:05.425436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:09.194687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:13.843515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:17.313512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:21.431554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:34.910638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:38.167039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:41.556902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:47.129303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:50.432225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:53.924184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:58.432259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:02.214371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:06.009044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:09.425765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:14.099632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:17.559286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:21.691085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:35.166927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:38.415187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:41.821447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:47.377443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:50.724437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:54.496583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:58.823297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:02.486074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:06.266620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:09.692411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:14.348469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:17.826628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:21.946178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:35.397356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:38.666123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:42.190991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:47.619776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:51.009222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:54.741789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:59.226044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:02.752571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:06.521367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:10.025994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:14.594709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:18.100200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:22.217290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:35.624940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:38.909106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:42.561190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:47.863695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:51.255814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:55.020595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:59.600367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:03.020009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:06.776260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:10.404270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:14.857069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:18.355845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:22.477258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:35.878860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:39.195585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:42.986675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:48.135432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:51.515759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:55.274709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:59.913900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:03.294492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:07.052800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:10.814114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:15.158904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:18.629843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:22.734001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:36.149782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:39.464755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:43.357667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:48.380766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:51.781729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:55.530998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:00.174188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:03.571852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:07.317095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:11.197315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:15.420508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:18.900988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:23.011607image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:36.373764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:39.719102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:43.694125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:48.614333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:52.057286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:55.786999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:00.420965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:03.824214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:07.589134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:11.580710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:15.679639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:19.578858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:23.277092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:36.624410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:39.990291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:44.069311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:48.885517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:52.302633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:56.159949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:00.679558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:04.085139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:07.853534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:11.981387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:15.970030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:19.839166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:23.532834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:36.868540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:40.273120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:44.483600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:49.178647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:52.578702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:56.538090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:00.949484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:04.346989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:08.141343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:12.377646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:16.247891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:20.135028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:23.797428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:37.157019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:40.522262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:44.868181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:49.411068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:52.851115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:56.950214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:01.197062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:04.609066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:08.386064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:12.734714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:16.497670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:20.376040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:24.181432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:37.410006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:40.787589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:46.358837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:49.664811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:53.138709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:57.326434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:01.466617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:04.880861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:08.676161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:13.119513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:16.766391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:20.643989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:24.579292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:37.664580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:41.071447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:46.630715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:49.937924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:53.412393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:35:57.708301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:01.724954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:05.169220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:08.942448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:13.501075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:17.062489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-08-13T14:36:20.904068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-08-13T14:36:42.552135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
52 Weeks High52 Weeks LowBetaCurrencyEPS AnnualExchangeMarket Cap (in M)Performance (52 weeks)PricePrice 52 Weeks AgoROI AnnualRésultat netSectorTotal assetsVolume 1 monthVolume 52 weeks
52 Weeks High1.0000.897-0.0360.0000.5620.0140.7340.3600.9060.9650.5270.5540.0000.2060.2810.228
52 Weeks Low0.8971.000-0.1340.0000.7080.0160.8310.4810.9830.9120.6620.6570.0000.2850.2280.169
Beta-0.036-0.1341.0000.000-0.2480.0920.028-0.070-0.096-0.071-0.270-0.2560.1250.1550.2430.265
Currency0.0000.0000.0001.0000.5610.0000.0000.0000.0000.0000.0000.3520.0550.0000.0000.000
EPS Annual0.5620.708-0.2480.5611.0000.0860.6030.3970.6830.5870.8520.8350.0510.2450.1220.079
Exchange0.0140.0160.0920.0000.0861.0000.0460.0430.0250.0000.0180.0760.4200.0750.0000.000
Market Cap (in M)0.7340.8310.0280.0000.6030.0461.0000.5130.8460.7300.5420.5640.0240.7140.6040.569
Performance (52 weeks)0.3600.481-0.0700.0000.3970.0430.5131.0000.5750.2440.3660.3860.0200.2060.0830.051
Price0.9060.983-0.0960.0000.6830.0250.8460.5751.0000.8930.6340.6360.0000.2920.2500.189
Price 52 Weeks Ago0.9650.912-0.0710.0000.5870.0000.7300.2440.8931.0000.5520.5650.0210.2160.2700.208
ROI Annual0.5270.662-0.2700.0000.8520.0180.5420.3660.6340.5521.0000.7420.0410.1900.0870.046
Résultat net0.5540.657-0.2560.3520.8350.0760.5640.3860.6360.5650.7421.0000.0320.2200.1560.123
Sector0.0000.0000.1250.0550.0510.4200.0240.0200.0000.0210.0410.0321.0000.0380.0000.016
Total assets0.2060.2850.1550.0000.2450.0750.7140.2060.2920.2160.1900.2200.0381.0000.7710.780
Volume 1 month0.2810.2280.2430.0000.1220.0000.6040.0830.2500.2700.0870.1560.0000.7711.0000.942
Volume 52 weeks0.2280.1690.2650.0000.0790.0000.5690.0510.1890.2080.0460.1230.0160.7800.9421.000

Missing values

2024-08-13T14:36:25.146206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-13T14:36:26.109782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SymbolCompany NamePriceMarket Cap (in M)BetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualROI Annual
0TRNSTranscat Inc116.29001051.5526920.93198950909.83794572982.875000147.11584.4500NASDAQ0.211360US15106000.0IndustrialsIndustrial Distribution96.050003USD2.553100e+071.63405.95
1ACRVAcrivon Therapeutics Inc7.1800217.9911340.711853234007.61660174267.79166712.8503.1900NASDAQ-0.381850US-64118000.0HealthcareBiotechnology11.600000USD1.320950e+08-2.7352-49.83
2COLMColumbia Sportswear Co81.83504781.3574870.628373461903.913043556895.41666787.23066.0100NASDAQ0.098148US227407008.0Consumer CyclicalApparel Manufacturing74.540024USD1.250472e+094.092912.97
3MOVEMovano Inc0.371436.5252241.216491151308.146245248723.3750001.4000.2660NASDAQ-0.715289US-27907000.0HealthcareMedical Devices1.300000USD3.505800e+07-0.6339-837.14
4HCKTHackett Group Inc25.5550711.3564920.40413797656.142292127029.33333327.68020.2300NASDAQ0.090217US34749000.0TechnologyInformation Technology Services23.445827USD5.962300e+071.235727.81
5HCWBHCW Biologics Inc0.598022.315802-0.01708215476.4584987464.3333332.1700.5710NASDAQ-0.707849US-27391652.0HealthcareBiotechnology2.040000USD4.048037e+07-0.6956-126.45
6MEIPMEI Pharma Inc3.390022.786971-1.26500552806.442688239755.4166677.8402.7293NASDAQ-0.375572US26155000.0HealthcareBiotechnology5.421965USD3.602900e+07-4.7808-129.43
7LFUSLittelfuse Inc241.95005987.8587211.125715118391.739130100371.250000275.450212.8000NASDAQ-0.060917US194587008.0TechnologyElectronic Components257.600555USD5.715050e+0810.33727.74
8ITRIItron Inc97.66004423.6650571.489375448229.138340737998.833333112.78056.1300NASDAQ0.392232US187596992.0TechnologyScientific & Technical Instruments70.209999USD8.822170e+082.11465.49
9AOSLAlpha and Omega Semiconductor Ltd34.97001036.4660081.833859202726.280632512560.37500047.45019.3800NASDAQ0.019588US-11081000.0TechnologySemiconductors34.299999USD2.058550e+080.42031.32
SymbolCompany NamePriceMarket Cap (in M)BetaVolume 52 weeksVolume 1 month52 Weeks High52 Weeks LowExchangePerformance (52 weeks)CountryRésultat netSectorIndustryPrice 52 Weeks AgoCurrencyTotal assetsEPS AnnualROI Annual
4472BOXBox Inc27.514002.3695710.4103471.836657e+061.355296e+0630.9723.5650NYSE-0.094119US1.070040e+08TechnologySoftware - Infrastructure30.360001USD144976992.00.868429.88
4473NSCNorfolk Southern Corp239.6254177.2276120.8009471.256250e+061.239665e+06263.66183.0900NYSE0.137450US1.791000e+09IndustrialsRailroads210.738556USD226096000.08.03436.10
4474CPACopa Holdings SA88.123673.4571441.1823493.297429e+053.127565e+05114.0078.1200NYSE-0.051162PA6.713850e+08IndustrialsAirlines92.858147USD30748900.012.779613.29
4475GPORGulfport Energy Corp138.142501.3117090.9882212.022063e+052.470174e+05165.13108.8400NYSE0.212501US7.523250e+08EnergyOil & Gas E&P113.989998USD18107100.077.818051.19
4476BABoeing Co167.91103460.6274121.1499397.051050e+066.611630e+06267.54159.7000NYSE-0.288335US-3.441000e+09IndustrialsAerospace & Defense235.720001USD616166976.0-3.667973.73
4477IVZInvesco Ltd16.167272.5241281.2013634.633188e+064.554791e+0618.2812.4800NYSE0.027753US-3.372000e+08Financial ServicesAsset Management15.724797USD450032000.0-0.2131-0.42
4478FBPFirst BanCorp19.743285.2710250.9282111.117185e+061.213457e+0622.1212.7150NYSE0.347341PR3.108070e+08Financial ServicesBanks - Regional14.663054USD163864992.01.709418.25
4479SNDASonida Senior Living Inc29.36418.1133200.8845681.800198e+042.690000e+0434.266.8900NYSE1.992714US-2.302900e+07HealthcareMedical Care Facilities9.840000USD14240900.0-3.1104-3.80
4480COHRCoherent Corp63.349656.8805423.4231482.326590e+062.209304e+0680.9128.4700NYSE0.403250US-4.145670e+08TechnologyScientific & Technical Instruments45.180000USD152460992.0-1.8859-2.24
4481TWLOTwilio Inc60.419701.8364001.5282492.782786e+062.422213e+0678.1649.8561NYSE-0.024610US-5.943220e+08TechnologySoftware - Infrastructure61.930000USD160600000.0-5.5389-9.46